The Threats of Artificial Intelligence Scale (TAI). Development, Measurement and Test Over Three Application Domains

TL;DR

This paper introduces the Threats of AI (TAI) scale, a measurement tool designed to assess public threat perceptions of AI across different functional classes and application domains, validated through a survey study.

Contribution

It develops and empirically tests a new, domain- and function-sensitive scale for measuring AI threat perceptions, filling a gap in existing research tools.

Findings

The TAI scale shows strong internal consistency and factorial validity.

The scale effectively captures threat perceptions across three AI application domains.

Empirical data support the scale's structural robustness and applicability.

Abstract

In recent years Artificial Intelligence (AI) has gained much popularity, with the scientific community as well as with the public. AI is often ascribed many positive impacts for different social domains such as medicine and the economy. On the other side, there is also growing concern about its precarious impact on society and individuals. Several opinion polls frequently query the public fear of autonomous robots and artificial intelligence (FARAI), a phenomenon coming also into scholarly focus. As potential threat perceptions arguably vary with regard to the reach and consequences of AI functionalities and the domain of application, research still lacks necessary precision of a respective measurement that allows for wide-spread research applicability. We propose a fine-grained scale to measure threat perceptions of AI that accounts for four functional classes of AI systems and is applicable to various domains of AI applications. Using a standardized questionnaire in a survey study (N=891), we evaluate the scale over three distinct AI domains (loan origination, job recruitment and medical treatment). The data support the dimensional structure of the proposed Threats of AI (TAI) scale as well as the internal consistency and factoral validity of the indicators. Implications of the results and the empirical application of the scale are discussed in detail. Recommendations for further empirical use of the TAI scale are provided.